Utilizing domain knowledge: Robust machine learning for building energy performance prediction with small, inconsistent datasets

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Xia Chen
  • Manav Mahan Singh
  • Philipp Geyer

Externe Organisationen

  • Technische Universität München (TUM)
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer111774
Seitenumfang10
FachzeitschriftKnowledge-based systems
Jahrgang294
Frühes Online-Datum5 Apr. 2024
PublikationsstatusVeröffentlicht - 21 Juni 2024

Abstract

Machine learning (ML) applications often require large datasets, a requirement that can pose a major challenge in fields where data is sparse or inconsistent. To address this issue, we propose a novel approach that combines prior knowledge with data-driven methods to significantly reduce data dependency. This study represents a disentangled system compositionality knowledge by the method of Component-Based Machine Learning (CBML) in the context of energy-efficient building engineering. In this way, CBML incorporates semantic domain knowledge within the structure of a data-driven model. To understand the advantage of CBML, we conducted a case experiment to assess the effectiveness of this knowledge-encoded ML approach in scenarios with sparse data input (1 % - 0.0125 % sampling rate) and several typical ML methods. Our findings reveal three key advantages of this approach over traditional ML methods: 1) It significantly improves the robustness of ML models when dealing with extremely small and inconsistent datasets; 2) It allows for efficient utilization of data from diverse record collections; 3) It can handle incomplete data while maintaining high interpretability and reducing training time. These features offer a promising solution to the challenges associated with deploying data-intensive methods and contribute to more efficient real-world data usage. Additionally, we outline four essential prerequisites to ensure the successful integration of prior knowledge and ML generalization in target scenarios and open-sourced the code and dataset for community reproduction.

ASJC Scopus Sachgebiete

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Utilizing domain knowledge: Robust machine learning for building energy performance prediction with small, inconsistent datasets. / Chen, Xia; Singh, Manav Mahan; Geyer, Philipp.
in: Knowledge-based systems, Jahrgang 294, 111774, 21.06.2024.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Chen X, Singh MM, Geyer P. Utilizing domain knowledge: Robust machine learning for building energy performance prediction with small, inconsistent datasets. Knowledge-based systems. 2024 Jun 21;294:111774. Epub 2024 Apr 5. doi: 10.1016/j.knosys.2024.111774
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AU - Singh, Manav Mahan

AU - Geyer, Philipp

N1 - Funding Information: We acknowledge the German Research Foundation (DFG) support for funding the project under grant GE 1652/3-2 in the Researcher Unit FOR 2363 and under Heisenberg grant GE 1652/4-1 .

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